Combining Vis–NIR hyperspectral imagery and legacy measured soil profiles to map subsurface soil properties in a Mediterranean area (Cap-Bon, Tunisia)

Lagacherie, Philippe; Sneep, Anne-Ruth; Gomez, Cécile; Bacha, Sinan; Coulouma, Guillaume; Hamrouni, Mohamed Hédi; Mekki, Insaf

doi:10.1016/j.geoderma.2013.06.005

Cited by 38 publications

(21 citation statements)

References 25 publications

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“…Regarding the model's performance, the results can be considered satisfactory; besides that, few studies in literature have used RF to predict soil CEC, and no one has used only remote sensing data as main covariates. The present results showed worst performance from statistical indexes when compared with those obtained by Lagacherie et al [61], which reached 79% for Var ex for the layer between 15 and 30 cm and 3.4 cmol c kg À1 for RMSE, using as input data terrain attributes and hyperspectral data in the visible and near infrared (AISA-Dual) with 5 m of spatial resolution. The difference from this study may be related to the coarser spatial resolution of images from Landsat 5 (30 m), in comparison with Lagacherie et al [61] who used hyperspectral data (5 m).…”

Section: Predictive Modelscontrasting

confidence: 89%

“…The present results showed worst performance from statistical indexes when compared with those obtained by Lagacherie et al [61], which reached 79% for Var ex for the layer between 15 and 30 cm and 3.4 cmol c kg À1 for RMSE, using as input data terrain attributes and hyperspectral data in the visible and near infrared (AISA-Dual) with 5 m of spatial resolution. The difference from this study may be related to the coarser spatial resolution of images from Landsat 5 (30 m), in comparison with Lagacherie et al [61] who used hyperspectral data (5 m). The influence of spatial resolution in the prediction of soil properties is reported by other studies [62,63].…”

Section: Predictive Modelscontrasting

confidence: 89%

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Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region

Pinheiro¹,

Xavier²,

Anjos³

et al. 2020

Multifunctionality and Impacts of Organic and Conventional Agriculture

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Brazilian semi-arid region is a recent frontier in the country for agribusiness. The objective of this study is to apply pedometric tools to zoning areas with distinct potential and limitations to agricultural purposes. The research was set in three main steps: (i) to compile a database with all complete profile data collection; (ii) to analyze the vertical variability of soil properties and select a set of soil key properties useful to define the land potential and limitations; and (iii) to classify the area according to potential for agriculture, considering a medium technological level of the farmers. The quantitative methods applied are supported by geographic information systems (GIS) and spatial statistics. The soil data compilation was based on legacy data, with corresponding topographical data and information from remote sensing images of the area. Tree-based and geostatistical algorithms were applied to predict the spatial variability of the soil key properties. The definition of management zones was based on Iso Cluster and Maximum Likelihood Classification tools. The results pointed three different management zones according to risks of salinization and requirements for irrigation control. The approach showed to be a simple and useful way to select and recommend primarily potential areas for agriculture based on soil properties.

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Section: Predictive Modelscontrasting

confidence: 89%

Section: Predictive Modelscontrasting

confidence: 89%

Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region

Pinheiro¹,

Xavier²,

Anjos³

et al. 2020

Multifunctionality and Impacts of Organic and Conventional Agriculture

View full text Add to dashboard Cite

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“…Many scientists have used this method to carry out related studies [39][40][41][42][43][44][45]. These studies have indicated that this method can be used to obtain good estimation results.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Jin

Wen

et al. 2015

Remote Sensing

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Abstract:The application of crop residue has become increasingly important for providing a barrier against water and wind erosion and improving soil organic matter content, infiltration, evaporation, temperature, and soil structure. The objectives of this work were to: (i) estimate maize residue cover (MRC) from Landsat-8 OLI images using seven vegetation indices (VIs) and eight textural features; and (ii) compare the VI method, textural feature method, and combination method (integration of textural features and spectral information) for estimating MRC with partial least squares regression (PLSR). The results showed that the normalized difference tillage index (NDTI), simple tillage index (STI), normalized difference index 7 (NDI7), and shortwave red normalized difference index (SRNDI) were significantly correlated with MRC. The MRC model based on NDTI outperformed (R 2 = 0.84 and RMSE = 12.33%) the models based on the other VIs. Band3mean, Band4mean, and Band5mean were highly correlated with MRC. The regression between Band3mean and MRC was stronger (R 2 = 0.71 and RMSE = 15.21%) than those between MRC and the other textural features. The MRC estimation accuracy using the combination method (R 2 = 0.96 and RMSE = 8.11%) was better than that based on only the VI (R 2 = 0.88 and RMSE = 11.34%) or textural feature (R 2 = 0.90 and RMSE = 9.82%) methods. The results suggest that the combination method can be used to estimate MRC on a regional scale. OPEN ACCESSRemote Sens. 2015, 7 14560

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“…Por exemplo, Nield et al (2007) utilizaram imagens do ETM+ Landsat 7 para mapear dois tipos de solos. A combinação de dados de sensoriamento remoto com dados de perfis de solos é ainda mais promissora, pois permite avaliar características subsuperficiais (Lagacherie et al, 2013).…”

Section: Introductionunclassified

Processamento de imagens RapidEye no mapeamento de uso do solo em ambiente de Mar de Morros

Costa

Antunes

Debiasi

et al. 2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi analisar características de imagem do satélite RapidEye, bem como avaliar sua qualidade, quanto à ortorretificação, e sua aplicação no mapeamento de uso do solo, em ambiente de Mar de Morros. As imagens passaram por correção atmosférica, seguida por avaliação dos seguintes aspectos: resposta espectral a diferentes alvos, na imagem sem correção e na corrigida; acurácia da imagem ortorretificada; e exatidão da classificação supervisionada. A correção atmosférica permitiu melhor avaliação da resposta espectral dos alvos. A avaliação da ortorretificação da imagem, quanto ao Padrão de Exatidão Cartográfico, enquadrou-se na classe C da escala 1:20.000. A classificação supervisionada quantificou áreas e percentagem de cada classe de uso de forma consistente. A distribuição das classes evidencia o efeito histórico do uso na degradação das terras.Termos para indexação: acurácia de imagem, conservação dos solos, correções radiométricas, sensoriamento remoto. RapidEye image processing for soil use mapping in rugged landscapeAbstract -The objective of this work was to analyse RapidEye satellite image characteristics, as well as to assess its orthorectification geometric accuracy and its application for land use mapping, in a rugged landscape. An atmospheric correction was applied to the images, which was followed by the assessment of the following aspects: spectral response to different targets in the uncorrected and corrected images; accuracy of the orthorectified image; and accuracy of the supervised classification. The atmospheric correction allowed a better evaluation of the spectral response of the targets. The geometrical evaluation according to the Map Accuracy Standards, classified the orthorectified image in class C at the 1:20,000 scale. The supervised classification consistently quantified the areas and the percentage of each soil use class. The distribution of classes highlights the historical effect of the use on land degradation.

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Combining Vis–NIR hyperspectral imagery and legacy measured soil profiles to map subsurface soil properties in a Mediterranean area (Cap-Bon, Tunisia)

Cited by 38 publications

References 25 publications

Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region

Pedometric Tools Applied to Zoning Management of Areas in Brazilian Semiarid Region

Estimation of Maize Residue Cover Using Landsat-8 OLI Image Spectral Information and Textural Features

Processamento de imagens RapidEye no mapeamento de uso do solo em ambiente de Mar de Morros

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